Robust Predictive Modeling Under Unseen Data Distribution Shifts: A Methodological Commentary
评述了预测模型在未见数据分布偏移下的风险，整理了迁移学习、领域泛化和分布鲁棒优化等应对方法，并给出实用建议，适合关注模型可靠性和可信AI的研究者与实践者。
Predictive models are widely used to support decision making, yet they are typically built assuming that future data will follow the same distribution as the training data. In practice, data distributions often change in unseen ways, leading to poor model performance and reduced reliability. This methodological commentary highlights the risks of unseen data distribution shifts and shows how they are frequently overlooked in predictive modeling practice. Drawing on transfer learning, domain generalization, and distributionally robust optimization, we organize existing approaches to handling data shifts and illustrate how uncertainty-aware modeling can be implemented in practice. We conclude with actionable recommendations to guide the design, evaluation, and use of predictive models in uncertain data environments. Our work has implications for policy and practice related to trustworthy and responsible artificial intelligence (AI), predictive modeling, and AI risk management.